Search Results for author: Paul M. Matthews

Found 13 papers, 5 papers with code

SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints

no code implementations2 Oct 2024 Berke Doga Basaran, Xinru Zhang, Paul M. Matthews, Wenjia Bai

Assessment of lesions and their longitudinal progression from brain magnetic resonance (MR) images plays a crucial role in diagnosing and monitoring multiple sclerosis (MS).

Lesion Segmentation Segmentation

A Personalised 3D+t Mesh Generative Model for Unveiling Normal Heart Dynamics

no code implementations20 Sep 2024 Mengyun Qiao, Kathryn A McGurk, Shuo Wang, Paul M. Matthews, Declan P O Regan, Wenjia Bai

To this end, we developed a novel conditional generative model, MeshHeart, to learn the distribution of cardiac shape and motion patterns.

Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data

1 code implementation28 Aug 2022 Mengyun Qiao, Berke Doga Basaran, Huaqi Qiu, Shuo Wang, Yi Guo, Yuanyuan Wang, Paul M. Matthews, Daniel Rueckert, Wenjia Bai

Understanding the morphological and functional changes of the heart during ageing is a key scientific question, the answer to which will help us define important risk factors of cardiovascular disease and monitor disease progression.

Anatomy

Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images

1 code implementation3 Aug 2022 Berke Doga Basaran, Mengyun Qiao, Paul M. Matthews, Wenjia Bai

In this work, we present a novel foreground-based generative method for modelling the local lesion characteristics that can both generate synthetic lesions on healthy images and synthesize subject-specific pseudo-healthy images from pathological images.

Brain Image Segmentation Data Augmentation +3

Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction

no code implementations5 Jul 2019 Wenjia Bai, Chen Chen, Giacomo Tarroni, Jinming Duan, Florian Guitton, Steffen E. Petersen, Yike Guo, Paul M. Matthews, Daniel Rueckert

In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks.

Image Segmentation Medical Image Analysis +5

Recurrent neural networks for aortic image sequence segmentation with sparse annotations

no code implementations1 Aug 2018 Wenjia Bai, Hideaki Suzuki, Chen Qin, Giacomo Tarroni, Ozan Oktay, Paul M. Matthews, Daniel Rueckert

In this work, we propose an image sequence segmentation algorithm by combining a fully convolutional network with a recurrent neural network, which incorporates both spatial and temporal information into the segmentation task.

Anatomy Medical Image Analysis +1

NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines

1 code implementation11 Jun 2018 Martin Rajchl, Nick Pawlowski, Daniel Rueckert, Paul M. Matthews, Ben Glocker

NeuroNet is a deep convolutional neural network mimicking multiple popular and state-of-the-art brain segmentation tools including FSL, SPM, and MALPEM.

Brain Image Segmentation Brain Segmentation +3

Learning-Based Quality Control for Cardiac MR Images

no code implementations25 Mar 2018 Giacomo Tarroni, Ozan Oktay, Wenjia Bai, Andreas Schuh, Hideaki Suzuki, Jonathan Passerat-Palmbach, Antonio de Marvao, Declan P. O'Regan, Stuart Cook, Ben Glocker, Paul M. Matthews, Daniel Rueckert

The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e. g. on UK Biobank, sensitivity and specificity respectively 88% and 99% for heart coverage estimation, 85% and 95% for motion detection), allowing their exclusion from the analysed dataset or the triggering of a new acquisition.

Motion Detection Specificity

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

1 code implementation25 Oct 2017 Wenjia Bai, Matthew Sinclair, Giacomo Tarroni, Ozan Oktay, Martin Rajchl, Ghislain Vaillant, Aaron M. Lee, Nay Aung, Elena Lukaschuk, Mihir M. Sanghvi, Filip Zemrak, Kenneth Fung, Jose Miguel Paiva, Valentina Carapella, Young Jin Kim, Hideaki Suzuki, Bernhard Kainz, Paul M. Matthews, Steffen E. Petersen, Stefan K. Piechnik, Stefan Neubauer, Ben Glocker, Daniel Rueckert

By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance on par with human experts in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images.

Mixed Neural Network Approach for Temporal Sleep Stage Classification

no code implementations15 Oct 2016 Hao Dong, Akara Supratak, Wei Pan, Chao Wu, Paul M. Matthews, Yike Guo

Use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical.

Classification EEG +1

Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks

no code implementations5 Oct 2016 Orestis Tsinalis, Paul M. Matthews, Yike Guo, Stefanos Zafeiriou

We used convolutional neural networks (CNNs) for automatic sleep stage scoring based on single-channel electroencephalography (EEG) to learn task-specific filters for classification without using prior domain knowledge.

EEG

Cannot find the paper you are looking for? You can Submit a new open access paper.